Software Behavior: Automatic Classification and its Applications

Show simple item record Bowring, James Frederick Rehg, James Matthew Harrold, Mary Jean 2005-03-28T15:07:59Z 2005-03-28T15:07:59Z 2003
dc.description.abstract A program's behavior is ultimately the collection of all its executions. This collection is diverse, unpredictable, and generally unbounded. Thus it is especially suited to statistical analysis and machine learning techniques. We explore the thesis that 1st- and 2nd-order Markov models of event-transitions are effective predictors of program behavior. We present a technique that models program executions as Markov models, and a clustering method for Markov models that aggregates multiple program executions, yielding a statistical description of program behaviors. With this approach, we can train classifiers to recognize specific behaviors emitted by an execution without knowledge of inputs or outcomes. We evaluate an application of active learning to the efficient refinement of our classifiers by conducting three empirical studies that explore a scenario illustrating automated test plan augmentation. We present a set of potential research questions and applications that our work suggests. en
dc.format.extent 453796 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en
dc.relation.ispartofseries CERCS;GIT-CERCS-03-19
dc.subject Classification techniques en
dc.subject Data clustering en
dc.subject Empirical studies en
dc.subject Event-transitions en
dc.subject Machine learning en
dc.subject Markov models en
dc.subject Modeling en
dc.subject Predictors en
dc.subject Program behavior en
dc.subject Program executions en
dc.subject Statistical analysis en
dc.subject Statistical description of program behaviors
dc.title Software Behavior: Automatic Classification and its Applications en
dc.type Technical Report en

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